# Copyright (c) 2018 Presto Labs Pte. Ltd.
# Author: jaewon

import datetime
import functools

from coin.base.datetime_util import to_datetime

from coin.exchange.binance.kr_rest.product import BinanceProduct
from coin.exchange.bitfinex_v2.kr_rest.product import BitfinexProduct
from coin.exchange.bitflyer_v1.kr_rest.futures_product import BitflyerFuturesProduct
from coin.exchange.bitmex.kr_rest.futures_product import BitmexFuturesProduct
from coin.exchange.gdax.kr_rest.product import GdaxProduct
from coin.exchange.huobi.kr_rest.product import HuobiProduct
from coin.exchange.okex_futures.kr_rest.futures_product import OkexFuturesProduct
from coin.exchange.upbit_v1.kr_rest.product import UpbitProduct

from coin.strategy.mm.simple_sim import result_util
from coin.strategy.mm.simple_sim.strategy.pass_unhedge_3 import PassUnhedgedSimStrategy
from coin.strategy.mm.simple_sim.profile.pass_unhedge_xbtusd_3 import (linear_sell_edge,
                                                                       linear_buy_edge)

ref_product = None
ref_product_true_book_funcs = None
trade_product = None
trade_product_true_book_funcs = None
price_multiplier_1 = None


def prepare(args, ref_ts):
  global ref_product, ref_product_true_book_funcs
  global trade_product, trade_product_true_book_funcs
  global price_multiplier_1

  trade_product = OkexFuturesProduct.FromStr('BTC-USD.QUARTER', current_datetime=ref_ts)
  trade_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(100.)[1][-1][0]),
                                   (lambda book: book.get_notional_bids_by_qty(100.)[1][-1][0]))

  args = dict(enumerate(args))
  ref_exchange = args.get(0, 'Bitmex')
  if ref_exchange == 'Bitmex':
    ref_product = BitmexFuturesProduct.FromStr('BTC-USD.PERPETUAL')
    ref_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(10000.)[1][-1][0]),
                                   (lambda book: book.get_notional_bids_by_qty(10000.)[1][-1][0]))

  elif ref_exchange == 'Binance':
    ref_product = BinanceProduct.FromStr('BTC-USDT')
    ref_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(1.)[1][-1][0]),
                                   (lambda book: book.get_notional_bids_by_qty(1.)[1][-1][0]))

  elif ref_exchange == 'Bitflyer':
    ref_product = BitflyerFuturesProduct.FromStr('BTC-JPY.PERPETUAL')
    ref_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(1.)[1][-1][0]),
                                   (lambda book: book.get_notional_bids_by_qty(1.)[1][-1][0]))
    price_multiplier_1 = 1 / 110.

  elif ref_exchange == 'Bitfinex':
    ref_product = BitfinexProduct.FromStr('BTC-USD')
    ref_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(1.)[1][-1][0]),
                                   (lambda book: book.get_notional_bids_by_qty(1.)[1][-1][0]))

  elif ref_exchange == 'Gdax':
    ref_product = GdaxProduct.FromStr('BTC-USD')
    ref_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(1.)[1][-1][0]),
                                   (lambda book: book.get_notional_bids_by_qty(1.)[1][-1][0]))

  elif ref_exchange == 'Huobi':
    ref_product = HuobiProduct.FromStr('BTC-USDT')
    ref_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(1.)[1][-1][0]),
                                   (lambda book: book.get_notional_bids_by_qty(1.)[1][-1][0]))

  elif ref_exchange == 'Upbit':
    ref_product = UpbitProduct.FromStr('BTC-KRW')
    ref_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(1.)[1][-1][0]),
                                   (lambda book: book.get_notional_bids_by_qty(1.)[1][-1][0]))
    price_multiplier_1 = 1 / 1120.

  else:
    raise ValueError('Unsupported: %s' % ref_exchange)


def get_products():
  return [ref_product, trade_product]


def get_machines():
  return ['feed-01.cn-hongkong.aliyun']


def get_time_ranges():
  ranges = []
  cur_dt = datetime.datetime(2018, 7, 11, 0, 0, 0)
  end_dt = datetime.datetime(2018, 7, 23, 0, 0, 0)
  while cur_dt < end_dt:
    ranges.append((cur_dt, cur_dt + datetime.timedelta(hours=24)))
    cur_dt += datetime.timedelta(hours=24)
  return ranges


def get_strategy(from_ts, to_ts):
  NS_PER_SECOND = (10**9)
  NS_PER_MINUTE = 60 * NS_PER_SECOND

  strategy_list = []

  for basis_ma_window in [5, 10, 15, 30]:  # [1, 2, 5, 10, 15]:  # , 25, 30, 45]:
    for edge_bp in [4, 5, 6, 7, 9, 11]:  # , 13, 15, 17]:
      for close_edge_bp in [edge_bp]:  # 2, edge_bp]:
        for agg_edge in [None]:
          stack = 5
          lot_size = 0.2
          delay = 5.0

          pricing_param = {
              'basis_ma_window': basis_ma_window * NS_PER_MINUTE,
              'tick': 0.01,
              'book_askt_func_1': ref_product_true_book_funcs[0],
              'book_bidt_func_1': ref_product_true_book_funcs[1],
              'book_askt_func_2': trade_product_true_book_funcs[0],
              'book_bidt_func_2': trade_product_true_book_funcs[1],
              'price_multiplier_1': price_multiplier_1
          }

          executor_param = {
              'lot_size': lot_size,
              'min_pos': -lot_size * stack,
              'max_pos': lot_size * stack,
              'maker_fee': 0. / 10000.,
              'taker_fee': 2. / 10000.,
              'execution_delay': delay * NS_PER_SECOND,
              'post_only': False,
              'trade_qty_func': (lambda trade: 100 * trade.qty / trade.price)
          }

          use_agg = 'without_agg'
          if agg_edge:
            use_agg = 'with_agg'
          name = '%02dm.%02dbp.%02dbp.%02dstack.%s.%s' % (basis_ma_window,
                                                          edge_bp,
                                                          close_edge_bp,
                                                          stack,
                                                          use_agg,
                                                          to_datetime(from_ts).strftime('%Y%m%d'))

          strategy = PassUnhedgedSimStrategy(trade_product,
                                             ref_product,
                                             functools.partial(linear_sell_edge,
                                                               edge_bp / 10000.,
                                                               close_edge_bp / 10000.,
                                                               1.),
                                             functools.partial(linear_buy_edge,
                                                               edge_bp / 10000.,
                                                               close_edge_bp / 10000.,
                                                               1.),
                                             pricing_param,
                                             executor_param,
                                             trade_after=basis_ma_window,
                                             name=name,
                                             agg_edge=agg_edge,
                                             feature_filepath=('out/feature.%s.csv' % name),
                                             fill_filepath=('out/fill.%s.csv' % name))
          strategy_list.append(strategy)

  return strategy_list


def get_strategy_result(strategy):
  return {'name': strategy.name, **strategy.get_summary()}


def aggregate_result(results):
  return result_util.aggregate_sim_result(results)
